Bibliographic record
Abstract
This book provides comprehensive insights on existing technologies and up-to-date advances in the field of waste management and treatment using algal-based technologies via different approaches and systems.Coverage includes: Process fundamentals of algae-based wastewater treatment, including metabolic modelling, algal species for resource recovery and algae/bacteria interactions.Critical insights on the status, major challenges and modern engineering solutions in microalgae-related wastewater treatment processes.Case studies for coculturing microalgae with methanotrophs for enhanced nutrient recovery from wastewater.Advanced ways for valorisation of algae-based processes by integrating them with other technologies such as anaerobic digestion, biogas upgradation and bioelectrochemical systems.Up-to-date information on modern biotechnological approaches for deriving value-added bioproducts and biopolymers from microalgae, including biofuels, pigments and nutraceuticals.This is an essential textbook for both undergraduate and graduate students pursuing degrees in environmental sciences, technologies, or engineering. Additionally, the book is equally useful for a broad audience, including researchers, engineers, and policy makers interested in the field of algal systems for waste and wastewater management. The book is also tailored to be used as an advanced manual for practitioners and consultancies working in the field of wastewater treatment and resource recovery.ISBN 9781789063530 (paperback)ISBN 9781789063547 (eBook)ISBN 9781789063554 (ePub)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.878 | 0.894 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".